Whiteley, Nick, Johansen, Adam M. and Godsill, Simon J., 1965- (2007) Efficient Monte Carlo filtering for discretely observed jumping processes. In: Workshop on Statistical Signal Processing (14th), Madison, Wis., 2007 Aug. 26-29 pp. 89-93.

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Abstract

This paper addresses a tracking problem in which the unobserved process is characterised by a collection of random jump times and associated random parameters. We construct a scheme for obtaining particle approximations to the posterior distributions of interest in the framework of sequential Monte Carlo (SMC) samplers [1]. We describe efficient sampling schemes and demonstrate that two existing schemes can be interpreted as particular cases of the proposed method. Results are provided which illustrate the performance improvements possible with our approach.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Monte Carlo method, Filters (Mathematics)
Book Title:	2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Date:	2007
Page Range:	pp. 89-93
Identification Number:	10.1109/SSP.2007.4301224
Status:	Peer Reviewed
Access rights to Published version:	Restricted or Subscription Access
Conference Paper Type:	Paper
Title of Event:	Workshop on Statistical Signal Processing (14th)
Type of Event:	Workshop
Location of Event:	Madison, Wis.
Date(s) of Event:	2007 Aug. 26-29
References:	[1] P. Del Moral, A. Doucet, and A. Jasra, “Sequential Monte Carlo samplers,” Journal of the Royal Statistical Society B, vol. 63, no. 3, pp. 411–436, 2006. [2] A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science. Springer Verlag, New York, 2001. [3] S.J. Godsill, J. Vermaak, K-F. Ng, and J-F. Li, “Models and algorithms for tracking of manoeuvring objects using variable rate particle filters,” Proc. IEEE, April 2007, (To Appear). [4] W.D. Blair, G.A. Watson, T. Kirubarajan, and Y. Bar-Shalom, “Benchmark for radar allocation and tracking in ECM,” IEEE Trans. AES, vol. 34, no. 4, pp. 1097–1114, October 1998. [5] S. J. Godsill and J. Vermaak, “Models and algorithms for tracking using trans-dimensional sequential Monte Carlo,” in Proc. IEEE ICASSP, 2004. [6] S.Maskell, “Joint tracking of manoeuvring targets and classification of their manoeuvrability,” EURASIP Journal on Applied Signal Processing, vol. 15, pp. 2339–2350, 2004. [7] P. Del Moral, A. Doucet, and A. Jasra, “Sequential Monte Carlo methods for Bayesian Computation,” in Bayesian Statistics 8. Oxford University Press, 2006. [8] A. Doucet, L. Montesano, and A. Jasra, “Optimal filtering for partially observed point processes using trans-dimensional sequential Monte Carlo,” in Proc. IEEE ICASSP, 2006. [9] A. Kong, J. S. Liu, and W. H. Wong, “Sequential imputations and Bayesian missing data problems,” Journal of the American Statistical Association, vol. 89, no. 425, pp. 278–288, March 1994. [10] A. Doucet, S. Godsill, and C. Andrieu, “On sequential Monte Carlo sampling methods for Bayesian filtering,” Statistics and Computing, vol. 10, pp. 197–208, 2000.
URI:	http://wrap.warwick.ac.uk/id/eprint/37286

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